wickra-data 0.7.0

Streaming-first technical indicators. Install with pip install wickra — no system dependencies.

Wickra is a multi-language technical-analysis library with a Rust core and bindings for Python, Node.js, and WebAssembly. Every indicator is a state machine that updates in O(1) per new data point, so live trading bots and historical backtests share the exact same implementation.

import numpy as np
import wickra as ta

# Batch: classic TA-Lib-style usage
prices = np.linspace(100, 200, 1000)
rsi = ta.RSI(14)
values = rsi.batch(prices)              # numpy array, NaN during warmup

# Streaming: same indicator, fed tick by tick
rsi = ta.RSI(14)
for price in live_feed:
    value = rsi.update(price)           # O(1) — no recomputation over history
    if value is not None and value > 70:
        print("overbought")

Documentation

Full documentation lives at docs.wickra.org:

• Quickstarts — Rust, Python, Node, WASM.
• Indicators — a per-indicator deep dive (formula, parameters, warmup) for every one of the 488 indicators; start at the indicators overview.
• Reference — warmup periods, streaming vs batch, indicator chaining, the data layer.
• Guides — Cookbook, TA-Lib migration, FAQ.

Why Wickra

Most TA libraries are fast, or multi-language, or broad. Wickra refuses to pick. It's the streaming-first engine built for the workload the others treat as an afterthought — live, tick-by-tick data — without giving up the breadth of a full batch library, and without making you reimplement your indicators four times to get there.

• The biggest streaming-native catalogue, period. 488 indicators across 24 families — candlesticks, harmonic & chart patterns, market profile, market breadth, Renko/Kagi/Point&Figure bars, Ehlers DSP cycles, risk/performance metrics — every single one updating in O(1) per tick. TA-Lib ships ~150 and none of them stream.
• One Rust core, four first-class targets. Native Python · Node.js · WebAssembly · Rust — identical math, identical results, zero per-language reimplementation and zero GIL bottleneck.
• Correct by construction, not by hope. Every update validates its input, runs a real warmup, and returns an Option so a single bad tick can't silently poison state. batch == streaming is bit-exact, fuzzed and 100 %-line-covered for all 488 indicators.
• Orders of magnitude faster where it counts. In streaming Wickra is 11–56× faster than the only other incremental peer and thousands of times faster than recompute-on-every-tick libraries. On batch it wins several rows outright and trades the simple recurrences (SMA, EMA, MACD) for its guarantees — and the losses are shown, not hidden.
• Install in one line, anywhere. pip install wickra / npm install wickra — precompiled wheels and binaries, no C toolchain, none of TA-Lib's setup pain. macOS · Linux · Windows.
• Batteries included. Indicator chaining, a streaming OHLCV CSV reader, and a live Binance kline feed ship in the box.
• Truly permissive. MIT OR Apache-2.0 — drop it straight into commercial and closed-source work.

Every other library forces one of those compromises. Wickra doesn't:

Broad, multi-language, streaming-native and honest about its trade-offs — at the same time. That's the combination no one else ships.

Why Wickra exists

Wickra started as a personal itch. The existing TA libraries never quite fit the projects I was building, so I decided to build one from the ground up — partly to learn, partly because I genuinely enjoy taking something that already exists and trying to do it differently (and, ideally, better). It's open source because the useful version of that itch is the one other people can build on too.

Benchmarks

Wickra updates every indicator in O(1) per tick. In streaming — the workload it is built for — it is 11–56× faster than the only other incremental peer and thousands of times faster than recompute-on-every-tick libraries. Batch is competitive: it wins several rows outright and trades a few µs elsewhere for None-warmup, NaN-safety and bit-exact batch == streaming.

Full tables (Rust + Python, streaming + batch) and how to reproduce them live in BENCHMARKS.md.

Indicators

488 streaming-first indicators across twenty-four families. Every one passes the batch == streaming equivalence test, reference-value tests, and reset semantics tests. Each has a per-indicator deep dive (formula, parameters, warmup) at docs.wickra.org.

Every candlestick pattern emits a signed per-bar value — +1.0 bullish, −1.0 bearish, 0.0 none — so the family drops straight into a feature matrix as one column each. Doji is direction-less by default (+1.0 / 0.0); construct it in signed mode (Doji::new().signed(), Doji(signed=True), new Doji(true)) for a dragonfly / gravestone ±1 reading.

Adding a new indicator means implementing one trait in Rust; all four bindings inherit it automatically.

Languages

Each binding ships several runnable examples (streaming, backtest, live feed); examples/README.md is the full cross-language index.

The wickra-core crate is unsafe-forbidden, so every binding inherits a memory-safe implementation.

Rust API

use wickra::{Indicator, BatchExt, Chain, Ema, Rsi, Sma};

// Streaming or batch — same trait, same code.
let mut sma = Sma::new(14)?;
let out: Vec<Option<f64>> = sma.batch(&[1.0, 2.0, 3.0, 4.0, 5.0]);

let mut rsi = Rsi::new(14)?;
for price in live_feed {
    if let Some(v) = rsi.update(price) {
        println!("RSI = {v}");
    }
}

// Compose indicators: RSI(7) on top of EMA(14).
let mut chain = Chain::new(Ema::new(14)?, Rsi::new(7)?);
chain.update(price);

Live data sources

wickra-data (separate crate, opt-in) ships:

• A streaming OHLCV CSV reader.
• A tick-to-candle aggregator with arbitrary timeframes.
• A candle resampler for multi-timeframe analysis (1m → 5m → 1h on the fly).
• A Binance Spot WebSocket kline adapter (feature live-binance).

use wickra::{Indicator, Rsi};
use wickra_data::live::binance::{BinanceKlineStream, Interval};

let mut stream = BinanceKlineStream::connect(&["BTCUSDT".into()], Interval::OneMinute).await?;
let mut rsi = Rsi::new(14)?;
while let Some(event) = stream.next_event().await? {
    if event.is_closed {
        if let Some(v) = rsi.update(event.candle.close) {
            println!("RSI = {v:.2}");
        }
    }
}

A Python live-trading example using the public websockets package lives at examples/python/live_trading.py.

Project layout

wickra/
├── crates/
│   ├── wickra-core/         core engine + all 488 indicators
│   ├── wickra/              top-level facade crate (publishes on crates.io) + benches/
│   ├── wickra-data/         CSV reader, tick aggregator, live exchange feeds
│   └── wickra-bench/        internal cross-library benchmark harness (not published)
├── bindings/
│   ├── python/              PyO3 + maturin (publishes on PyPI)
│   ├── node/                napi-rs (publishes on npm)
│   └── wasm/                wasm-bindgen (browsers, bundlers, Node)
├── examples/                examples/README.md indexes every language
│   ├── data/                real BTCUSDT OHLCV datasets, one per timeframe
│   ├── rust/                Rust workspace member (`wickra-examples`)
│   ├── python/              backtest, live trading, parallel assets, multi-tf
│   ├── node/                streaming, backtest, live trading (load `wickra`)
│   └── wasm/                browser demo for `wickra-wasm`
└── .github/workflows/       CI and release pipelines

Wickra's own regression benchmarks live in crates/wickra/benches/; the cross-library comparison against kand, ta-rs and yata lives in the internal crates/wickra-bench/ crate. Runnable Rust examples live in the workspace member crate at examples/rust/. There is no top-level benches/ directory.

Building everything from source

# Rust core + tests
cargo test --workspace
cargo clippy --workspace --all-targets -- -D warnings
cargo bench -p wickra           # Wickra's own regression benchmarks
cargo bench -p wickra-bench     # cross-library comparison (kand, ta-rs, yata)

# Python binding (requires Rust toolchain + maturin)
cd bindings/python
maturin develop --release
pytest

# WASM binding (requires wasm-pack + wasm32-unknown-unknown target)
wasm-pack build bindings/wasm --target web --release --features panic-hook

# Node binding (requires @napi-rs/cli)
cd bindings/node && npm install && npm run build && npm test

Testing

Every layer is covered; run the suites with the commands in Building everything from source.

• wickra-core: unit tests per indicator — textbook reference values (Wilder RSI, Bollinger Bands, MACD, ATR, Stochastic), batch == streaming equivalence, reset semantics, NaN/Inf handling, and property tests.
• wickra-data: unit tests for CSV decoding, the tick aggregator, the resampler, and the Binance payload parser.
• bindings/python: pytest covering smoke checks, streaming/batch equivalence, reference values, lifecycle, input validation, and dict/tuple candle inputs.
• bindings/node: node --test cases for batch, streaming, and reference values across all indicators.
• bindings/wasm: wasm-bindgen-test cases for constructors, equivalence, and reference values.

Contributing

Contributions are very welcome — issues, bug reports, ideas, and pull requests all land in the same place: https://github.com/wickra-lib/wickra.

A short orientation for first-time contributors:

• Adding an indicator. Implement the Indicator trait in crates/wickra-core/src/indicators/<name>.rs, wire it into indicators/mod.rs and the crate root, and add reference-value tests, a batch == streaming equivalence test, and (where it makes sense) a proptest. The four bindings inherit your indicator automatically once you expose it in the language wrappers.
• Fixing a numeric bug. Add a failing test that pins the textbook value first, then fix the math. Property tests in crates/wickra-core catch most regressions; please don't disable them.
• Improving a binding. Each binding lives under bindings/<lang> with its own tests; please keep the batch == streaming invariant.
• Style. cargo fmt --all + cargo clippy --workspace --all-targets -- -D warnings are CI gates; running them locally before pushing keeps reviews short.

For larger architectural changes, open an issue first so we can sketch the shape together before you invest the time.

License

Licensed under either of

• Apache License, Version 2.0 (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)
• MIT license (LICENSE-MIT or http://opensource.org/licenses/MIT)

at your option. Use it, fork it, modify it, redistribute it — commercially or not — file issues, send pull requests; all welcome.

Contribution

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.

Disclaimer

Wickra is an indicator toolkit, not a trading system. Values it computes are deterministic transforms of the input data — they are not financial advice and they do not predict the market. Any use of this library in a production trading context is at your own risk.

The library is provided as is, without warranty of any kind; see LICENSE for the full terms.